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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
July 9, 202610 min read -
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Hosted by Danielle Perszyk, cognitive scientist at Amazon, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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CoRL 2022 Workshop on Language and Robot Learning2022Household environments are visually diverse. Embodied agents performing Vision-and-Language Navigation (VLN) in the wild must be able to handle this diversity, while also following arbitrary language instructions. Recently, VisionLanguage models like CLIP have shown great performance on the task of zeroshot object recognition. In this work, we ask if these models are also capable of zero-shot language grounding
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NeurIPS 2022 Workshop on a Causal View on Dynamical Systems2022Seeking causal explanations in panel (or longitudinal/multivariate time-series) data is a difficult problem of both academic and industrial importance. Although there exists a large amount of literature on forward causal inference, where the treatment/outcome/covariates variables are well-defined, it is unclear how to answer the reverse question: which covariates have effects on the outcome? In this paper
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NeurIPS 2022 Workshop on Efficient Natural Language and Speech Processing (ENLSP)2022The use of multilingual language models for tasks in low and high-resource languages has been a success story in deep learning. In recent times, Arabic has been receiving widespread attention on account of its dialectal variance. While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a challenging problem owing to the lack of sufficient
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New Trends in Translation and Technology (NeTTT)2022Simultaneous machine translation (SimulMT) is a challenging task which aims to translate a source sequence to the target language with low latency. Despite significant progress in SimulMT, there has not been much work in the area of multilingual SimulMT where a single model is capable of translating between multiple language pairs. This paper studies SimulMT from a multilingual perspective. Through our
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NeurIPS 2022 Has It Trained Yet Workshop2022Modern-day deep learning models are trained efficiently at scale thanks to the widespread use of stochastic optimizers such as SGD and ADAM. These optimizers update the model weights iteratively based on a batch of uniformly sampled training data at each iteration. However, it has been previously observed that the training performance and overall generalization ability of the model can be significantly
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
